31b4973141
* Metadata filtering initial version * Metadata filtering initial version * Fixes linter issues * Cleanup code * Clean up and readme * Fix after review * Use UV in example * Merge main into feature/metadata-filtering
931 lines
36 KiB
Python
931 lines
36 KiB
Python
"""
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This file contains the core API for the LEANN project, now definitively updated
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with the correct, original embedding logic from the user's reference code.
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"""
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import json
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import logging
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import pickle
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import time
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import warnings
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, Literal, Optional, Union
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import numpy as np
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from leann.interface import LeannBackendSearcherInterface
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from .chat import get_llm
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from .interface import LeannBackendFactoryInterface
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from .metadata_filter import MetadataFilterEngine
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from .registry import BACKEND_REGISTRY
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logger = logging.getLogger(__name__)
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def get_registered_backends() -> list[str]:
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"""Get list of registered backend names."""
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return list(BACKEND_REGISTRY.keys())
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def compute_embeddings(
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chunks: list[str],
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model_name: str,
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mode: str = "sentence-transformers",
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use_server: bool = True,
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port: Optional[int] = None,
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is_build=False,
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) -> np.ndarray:
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"""
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Computes embeddings using different backends.
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Args:
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chunks: List of text chunks to embed
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model_name: Name of the embedding model
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mode: Embedding backend mode. Options:
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- "sentence-transformers": Use sentence-transformers library (default)
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- "mlx": Use MLX backend for Apple Silicon
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- "openai": Use OpenAI embedding API
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- "gemini": Use Google Gemini embedding API
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use_server: Whether to use embedding server (True for search, False for build)
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Returns:
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numpy array of embeddings
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"""
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if use_server:
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# Use embedding server (for search/query)
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if port is None:
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raise ValueError("port is required when use_server is True")
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return compute_embeddings_via_server(chunks, model_name, port=port)
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else:
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# Use direct computation (for build_index)
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from .embedding_compute import (
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compute_embeddings as compute_embeddings_direct,
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)
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return compute_embeddings_direct(
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chunks,
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model_name,
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mode=mode,
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is_build=is_build,
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)
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def compute_embeddings_via_server(chunks: list[str], model_name: str, port: int) -> np.ndarray:
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"""Computes embeddings using sentence-transformers.
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Args:
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chunks: List of text chunks to embed
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model_name: Name of the sentence transformer model
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"""
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logger.info(
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f"Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (via embedding server)..."
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)
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import msgpack
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import numpy as np
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import zmq
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# Connect to embedding server
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context = zmq.Context()
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socket = context.socket(zmq.REQ)
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socket.connect(f"tcp://localhost:{port}")
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# Send chunks to server for embedding computation
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request = chunks
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socket.send(msgpack.packb(request))
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# Receive embeddings from server
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response = socket.recv()
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embeddings_list = msgpack.unpackb(response)
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# Convert back to numpy array
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embeddings = np.array(embeddings_list, dtype=np.float32)
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socket.close()
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context.term()
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return embeddings
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@dataclass
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class SearchResult:
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id: str
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score: float
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text: str
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metadata: dict[str, Any] = field(default_factory=dict)
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class PassageManager:
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def __init__(
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self, passage_sources: list[dict[str, Any]], metadata_file_path: Optional[str] = None
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):
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self.offset_maps: dict[str, dict[str, int]] = {}
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self.passage_files: dict[str, str] = {}
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# Avoid materializing a single gigantic global map to reduce memory
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# footprint on very large corpora (e.g., 60M+ passages). Instead, keep
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# per-shard maps and do a lightweight per-shard lookup on demand.
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self._total_count: int = 0
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self.filter_engine = MetadataFilterEngine() # Initialize filter engine
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# Derive index base name for standard sibling fallbacks, e.g., <index_name>.passages.*
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index_name_base = None
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if metadata_file_path:
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meta_name = Path(metadata_file_path).name
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if meta_name.endswith(".meta.json"):
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index_name_base = meta_name[: -len(".meta.json")]
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for source in passage_sources:
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assert source["type"] == "jsonl", "only jsonl is supported"
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passage_file = source.get("path", "")
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index_file = source.get("index_path", "") # .idx file
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# Fix path resolution - relative paths should be relative to metadata file directory
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def _resolve_candidates(
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primary: str,
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relative_key: str,
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default_name: Optional[str],
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source_dict: dict[str, Any],
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) -> list[Path]:
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"""
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Build an ordered list of candidate paths. For relative paths specified in
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metadata, prefer resolution relative to the metadata file directory first,
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then fall back to CWD-based resolution, and finally to conventional
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sibling defaults (e.g., <index_base>.passages.idx / .jsonl).
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"""
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candidates: list[Path] = []
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# 1) Primary path
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if primary:
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p = Path(primary)
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if p.is_absolute():
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candidates.append(p)
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else:
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# Prefer metadata-relative resolution for relative paths
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if metadata_file_path:
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candidates.append(Path(metadata_file_path).parent / p)
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# Also consider CWD-relative as a fallback for legacy layouts
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candidates.append(Path.cwd() / p)
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# 2) metadata-relative explicit relative key (if present)
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if metadata_file_path and source_dict.get(relative_key):
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candidates.append(Path(metadata_file_path).parent / source_dict[relative_key])
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# 3) metadata-relative standard sibling filename
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if metadata_file_path and default_name:
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candidates.append(Path(metadata_file_path).parent / default_name)
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return candidates
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# Build candidate lists and pick first existing; otherwise keep last candidate for error message
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idx_default = f"{index_name_base}.passages.idx" if index_name_base else None
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idx_candidates = _resolve_candidates(
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index_file, "index_path_relative", idx_default, source
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)
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pas_default = f"{index_name_base}.passages.jsonl" if index_name_base else None
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pas_candidates = _resolve_candidates(passage_file, "path_relative", pas_default, source)
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def _pick_existing(cands: list[Path]) -> str:
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for c in cands:
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if c.exists():
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return str(c.resolve())
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# Fallback to last candidate (best guess) even if not exists; will error below
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return str(cands[-1].resolve()) if cands else ""
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index_file = _pick_existing(idx_candidates)
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passage_file = _pick_existing(pas_candidates)
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if not Path(index_file).exists():
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raise FileNotFoundError(f"Passage index file not found: {index_file}")
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with open(index_file, "rb") as f:
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offset_map: dict[str, int] = pickle.load(f)
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self.offset_maps[passage_file] = offset_map
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self.passage_files[passage_file] = passage_file
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self._total_count += len(offset_map)
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def get_passage(self, passage_id: str) -> dict[str, Any]:
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# Fast path: check each shard map (there are typically few shards).
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# This avoids building a massive combined dict while keeping lookups
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# bounded by the number of shards.
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for passage_file, offset_map in self.offset_maps.items():
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try:
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offset = offset_map[passage_id]
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with open(passage_file, encoding="utf-8") as f:
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f.seek(offset)
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return json.loads(f.readline())
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except KeyError:
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continue
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raise KeyError(f"Passage ID not found: {passage_id}")
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def filter_search_results(
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self,
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search_results: list[SearchResult],
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metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]],
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) -> list[SearchResult]:
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"""
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Apply metadata filters to search results.
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Args:
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search_results: List of SearchResult objects
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metadata_filters: Filter specifications to apply
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Returns:
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Filtered list of SearchResult objects
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"""
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if not metadata_filters:
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return search_results
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logger.debug(f"Applying metadata filters to {len(search_results)} results")
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# Convert SearchResult objects to dictionaries for the filter engine
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result_dicts = []
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for result in search_results:
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result_dicts.append(
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{
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"id": result.id,
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"score": result.score,
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"text": result.text,
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"metadata": result.metadata,
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}
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)
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# Apply filters using the filter engine
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filtered_dicts = self.filter_engine.apply_filters(result_dicts, metadata_filters)
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# Convert back to SearchResult objects
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filtered_results = []
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for result_dict in filtered_dicts:
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filtered_results.append(
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SearchResult(
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id=result_dict["id"],
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score=result_dict["score"],
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text=result_dict["text"],
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metadata=result_dict["metadata"],
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)
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)
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logger.debug(f"Filtered results: {len(filtered_results)} remaining")
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return filtered_results
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def __len__(self) -> int:
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return self._total_count
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class LeannBuilder:
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def __init__(
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self,
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backend_name: str,
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embedding_model: str = "facebook/contriever",
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dimensions: Optional[int] = None,
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embedding_mode: str = "sentence-transformers",
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**backend_kwargs,
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):
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self.backend_name = backend_name
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# Normalize incompatible combinations early (for consistent metadata)
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if backend_name == "hnsw":
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is_recompute = backend_kwargs.get("is_recompute", True)
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is_compact = backend_kwargs.get("is_compact", True)
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if is_recompute is False and is_compact is True:
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warnings.warn(
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"HNSW with is_recompute=False requires non-compact storage. Forcing is_compact=False.",
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UserWarning,
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stacklevel=2,
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)
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backend_kwargs["is_compact"] = False
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backend_factory: Optional[LeannBackendFactoryInterface] = BACKEND_REGISTRY.get(backend_name)
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if backend_factory is None:
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raise ValueError(f"Backend '{backend_name}' not found or not registered.")
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self.backend_factory = backend_factory
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self.embedding_model = embedding_model
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self.dimensions = dimensions
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self.embedding_mode = embedding_mode
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# Check if we need to use cosine distance for normalized embeddings
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normalized_embeddings_models = {
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# OpenAI models
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("openai", "text-embedding-ada-002"),
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("openai", "text-embedding-3-small"),
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("openai", "text-embedding-3-large"),
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# Voyage AI models
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("voyage", "voyage-2"),
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("voyage", "voyage-3"),
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("voyage", "voyage-large-2"),
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("voyage", "voyage-multilingual-2"),
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("voyage", "voyage-code-2"),
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# Cohere models
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("cohere", "embed-english-v3.0"),
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("cohere", "embed-multilingual-v3.0"),
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("cohere", "embed-english-light-v3.0"),
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("cohere", "embed-multilingual-light-v3.0"),
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}
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# Also check for patterns in model names
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is_normalized = False
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current_model_lower = embedding_model.lower()
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current_mode_lower = embedding_mode.lower()
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# Check exact matches
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for mode, model in normalized_embeddings_models:
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if (current_mode_lower == mode and current_model_lower == model) or (
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mode in current_mode_lower and model in current_model_lower
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):
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is_normalized = True
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break
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# Check patterns
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if not is_normalized:
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# OpenAI patterns
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if "openai" in current_mode_lower or "openai" in current_model_lower:
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if any(
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pattern in current_model_lower
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for pattern in ["text-embedding", "ada", "3-small", "3-large"]
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):
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is_normalized = True
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# Voyage patterns
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elif "voyage" in current_mode_lower or "voyage" in current_model_lower:
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is_normalized = True
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# Cohere patterns
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elif "cohere" in current_mode_lower or "cohere" in current_model_lower:
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if "embed" in current_model_lower:
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is_normalized = True
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# Handle distance metric
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if is_normalized and "distance_metric" not in backend_kwargs:
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backend_kwargs["distance_metric"] = "cosine"
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warnings.warn(
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f"Detected normalized embeddings model '{embedding_model}' with mode '{embedding_mode}'. "
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f"Automatically setting distance_metric='cosine' for optimal performance. "
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f"Normalized embeddings (L2 norm = 1) should use cosine similarity instead of MIPS.",
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UserWarning,
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stacklevel=2,
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)
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elif is_normalized and backend_kwargs.get("distance_metric", "").lower() != "cosine":
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current_metric = backend_kwargs.get("distance_metric", "mips")
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warnings.warn(
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f"Warning: Using '{current_metric}' distance metric with normalized embeddings model "
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f"'{embedding_model}' may lead to suboptimal search results. "
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f"Consider using 'cosine' distance metric for better performance.",
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UserWarning,
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stacklevel=2,
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)
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self.backend_kwargs = backend_kwargs
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self.chunks: list[dict[str, Any]] = []
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def add_text(self, text: str, metadata: Optional[dict[str, Any]] = None):
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if metadata is None:
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metadata = {}
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passage_id = metadata.get("id", str(len(self.chunks)))
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chunk_data = {"id": passage_id, "text": text, "metadata": metadata}
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self.chunks.append(chunk_data)
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def build_index(self, index_path: str):
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if not self.chunks:
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raise ValueError("No chunks added.")
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# Filter out invalid/empty text chunks early to keep passage and embedding counts aligned
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valid_chunks: list[dict[str, Any]] = []
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skipped = 0
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for chunk in self.chunks:
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text = chunk.get("text", "")
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if isinstance(text, str) and text.strip():
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valid_chunks.append(chunk)
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else:
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skipped += 1
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if skipped > 0:
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print(
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f"Warning: Skipping {skipped} empty/invalid text chunk(s). Processing {len(valid_chunks)} valid chunks"
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)
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self.chunks = valid_chunks
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if not self.chunks:
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raise ValueError("All provided chunks are empty or invalid. Nothing to index.")
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if self.dimensions is None:
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self.dimensions = len(
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compute_embeddings(
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["dummy"],
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self.embedding_model,
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self.embedding_mode,
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use_server=False,
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)[0]
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)
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path = Path(index_path)
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index_dir = path.parent
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index_name = path.name
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index_dir.mkdir(parents=True, exist_ok=True)
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passages_file = index_dir / f"{index_name}.passages.jsonl"
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offset_file = index_dir / f"{index_name}.passages.idx"
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offset_map = {}
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with open(passages_file, "w", encoding="utf-8") as f:
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try:
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from tqdm import tqdm
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chunk_iterator = tqdm(self.chunks, desc="Writing passages", unit="chunk")
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except ImportError:
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chunk_iterator = self.chunks
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for chunk in chunk_iterator:
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offset = f.tell()
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json.dump(
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{
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"id": chunk["id"],
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"text": chunk["text"],
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"metadata": chunk["metadata"],
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},
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f,
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ensure_ascii=False,
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)
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f.write("\n")
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offset_map[chunk["id"]] = offset
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with open(offset_file, "wb") as f:
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pickle.dump(offset_map, f)
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texts_to_embed = [c["text"] for c in self.chunks]
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embeddings = compute_embeddings(
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texts_to_embed,
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self.embedding_model,
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self.embedding_mode,
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use_server=False,
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is_build=True,
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)
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string_ids = [chunk["id"] for chunk in self.chunks]
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current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}
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builder_instance = self.backend_factory.builder(**current_backend_kwargs)
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builder_instance.build(embeddings, string_ids, index_path, **current_backend_kwargs)
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leann_meta_path = index_dir / f"{index_name}.meta.json"
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meta_data = {
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"version": "1.0",
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"backend_name": self.backend_name,
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"embedding_model": self.embedding_model,
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"dimensions": self.dimensions,
|
|
"backend_kwargs": self.backend_kwargs,
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|
"embedding_mode": self.embedding_mode,
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|
"passage_sources": [
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{
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"type": "jsonl",
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# Preserve existing relative file names (backward-compatible)
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|
"path": passages_file.name,
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|
"index_path": offset_file.name,
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|
# Add optional redundant relative keys for remote build portability (non-breaking)
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|
"path_relative": passages_file.name,
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|
"index_path_relative": offset_file.name,
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}
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],
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}
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|
|
|
# Add storage status flags for HNSW backend
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|
if self.backend_name == "hnsw":
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is_compact = self.backend_kwargs.get("is_compact", True)
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is_recompute = self.backend_kwargs.get("is_recompute", True)
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meta_data["is_compact"] = is_compact
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meta_data["is_pruned"] = (
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is_compact and is_recompute
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) # Pruned only if compact and recompute
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with open(leann_meta_path, "w", encoding="utf-8") as f:
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json.dump(meta_data, f, indent=2)
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|
|
def build_index_from_embeddings(self, index_path: str, embeddings_file: str):
|
|
"""
|
|
Build an index from pre-computed embeddings stored in a pickle file.
|
|
|
|
Args:
|
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index_path: Path where the index will be saved
|
|
embeddings_file: Path to pickle file containing (ids, embeddings) tuple
|
|
"""
|
|
# Load pre-computed embeddings
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|
with open(embeddings_file, "rb") as f:
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data = pickle.load(f)
|
|
|
|
if not isinstance(data, tuple) or len(data) != 2:
|
|
raise ValueError(
|
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f"Invalid embeddings file format. Expected tuple with 2 elements, got {type(data)}"
|
|
)
|
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|
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ids, embeddings = data
|
|
|
|
if not isinstance(embeddings, np.ndarray):
|
|
raise ValueError(f"Expected embeddings to be numpy array, got {type(embeddings)}")
|
|
|
|
if len(ids) != embeddings.shape[0]:
|
|
raise ValueError(
|
|
f"Mismatch between number of IDs ({len(ids)}) and embeddings ({embeddings.shape[0]})"
|
|
)
|
|
|
|
# Validate/set dimensions
|
|
embedding_dim = embeddings.shape[1]
|
|
if self.dimensions is None:
|
|
self.dimensions = embedding_dim
|
|
elif self.dimensions != embedding_dim:
|
|
raise ValueError(f"Dimension mismatch: expected {self.dimensions}, got {embedding_dim}")
|
|
|
|
logger.info(
|
|
f"Building index from precomputed embeddings: {len(ids)} items, {embedding_dim} dimensions"
|
|
)
|
|
|
|
# Ensure we have text data for each embedding
|
|
if len(self.chunks) != len(ids):
|
|
# If no text chunks provided, create placeholder text entries
|
|
if not self.chunks:
|
|
logger.info("No text chunks provided, creating placeholder entries...")
|
|
for id_val in ids:
|
|
self.add_text(
|
|
f"Document {id_val}",
|
|
metadata={"id": str(id_val), "from_embeddings": True},
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Number of text chunks ({len(self.chunks)}) doesn't match number of embeddings ({len(ids)})"
|
|
)
|
|
|
|
# Build file structure
|
|
path = Path(index_path)
|
|
index_dir = path.parent
|
|
index_name = path.name
|
|
index_dir.mkdir(parents=True, exist_ok=True)
|
|
passages_file = index_dir / f"{index_name}.passages.jsonl"
|
|
offset_file = index_dir / f"{index_name}.passages.idx"
|
|
|
|
# Write passages and create offset map
|
|
offset_map = {}
|
|
with open(passages_file, "w", encoding="utf-8") as f:
|
|
for chunk in self.chunks:
|
|
offset = f.tell()
|
|
json.dump(
|
|
{
|
|
"id": chunk["id"],
|
|
"text": chunk["text"],
|
|
"metadata": chunk["metadata"],
|
|
},
|
|
f,
|
|
ensure_ascii=False,
|
|
)
|
|
f.write("\n")
|
|
offset_map[chunk["id"]] = offset
|
|
|
|
with open(offset_file, "wb") as f:
|
|
pickle.dump(offset_map, f)
|
|
|
|
# Build the vector index using precomputed embeddings
|
|
string_ids = [str(id_val) for id_val in ids]
|
|
current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}
|
|
builder_instance = self.backend_factory.builder(**current_backend_kwargs)
|
|
builder_instance.build(embeddings, string_ids, index_path)
|
|
|
|
# Create metadata file
|
|
leann_meta_path = index_dir / f"{index_name}.meta.json"
|
|
meta_data = {
|
|
"version": "1.0",
|
|
"backend_name": self.backend_name,
|
|
"embedding_model": self.embedding_model,
|
|
"dimensions": self.dimensions,
|
|
"backend_kwargs": self.backend_kwargs,
|
|
"embedding_mode": self.embedding_mode,
|
|
"passage_sources": [
|
|
{
|
|
"type": "jsonl",
|
|
# Preserve existing relative file names (backward-compatible)
|
|
"path": passages_file.name,
|
|
"index_path": offset_file.name,
|
|
# Add optional redundant relative keys for remote build portability (non-breaking)
|
|
"path_relative": passages_file.name,
|
|
"index_path_relative": offset_file.name,
|
|
}
|
|
],
|
|
"built_from_precomputed_embeddings": True,
|
|
"embeddings_source": str(embeddings_file),
|
|
}
|
|
|
|
# Add storage status flags for HNSW backend
|
|
if self.backend_name == "hnsw":
|
|
is_compact = self.backend_kwargs.get("is_compact", True)
|
|
is_recompute = self.backend_kwargs.get("is_recompute", True)
|
|
meta_data["is_compact"] = is_compact
|
|
meta_data["is_pruned"] = is_compact and is_recompute
|
|
|
|
with open(leann_meta_path, "w", encoding="utf-8") as f:
|
|
json.dump(meta_data, f, indent=2)
|
|
|
|
logger.info(f"Index built successfully from precomputed embeddings: {index_path}")
|
|
|
|
|
|
class LeannSearcher:
|
|
def __init__(self, index_path: str, enable_warmup: bool = False, **backend_kwargs):
|
|
# Fix path resolution for Colab and other environments
|
|
if not Path(index_path).is_absolute():
|
|
index_path = str(Path(index_path).resolve())
|
|
|
|
self.meta_path_str = f"{index_path}.meta.json"
|
|
if not Path(self.meta_path_str).exists():
|
|
parent_dir = Path(index_path).parent
|
|
print(
|
|
f"Leann metadata file not found at {self.meta_path_str}, and you may need to rm -rf {parent_dir}"
|
|
)
|
|
# highlight in red the filenotfound error
|
|
raise FileNotFoundError(
|
|
f"Leann metadata file not found at {self.meta_path_str}, \033[91m you may need to rm -rf {parent_dir}\033[0m"
|
|
)
|
|
with open(self.meta_path_str, encoding="utf-8") as f:
|
|
self.meta_data = json.load(f)
|
|
backend_name = self.meta_data["backend_name"]
|
|
self.embedding_model = self.meta_data["embedding_model"]
|
|
# Support both old and new format
|
|
self.embedding_mode = self.meta_data.get("embedding_mode", "sentence-transformers")
|
|
# Delegate portability handling to PassageManager
|
|
self.passage_manager = PassageManager(
|
|
self.meta_data.get("passage_sources", []), metadata_file_path=self.meta_path_str
|
|
)
|
|
# Preserve backend name for conditional parameter forwarding
|
|
self.backend_name = backend_name
|
|
backend_factory = BACKEND_REGISTRY.get(backend_name)
|
|
if backend_factory is None:
|
|
raise ValueError(f"Backend '{backend_name}' not found.")
|
|
final_kwargs = {**self.meta_data.get("backend_kwargs", {}), **backend_kwargs}
|
|
final_kwargs["enable_warmup"] = enable_warmup
|
|
self.backend_impl: LeannBackendSearcherInterface = backend_factory.searcher(
|
|
index_path, **final_kwargs
|
|
)
|
|
|
|
def search(
|
|
self,
|
|
query: str,
|
|
top_k: int = 5,
|
|
complexity: int = 64,
|
|
beam_width: int = 1,
|
|
prune_ratio: float = 0.0,
|
|
recompute_embeddings: bool = True,
|
|
pruning_strategy: Literal["global", "local", "proportional"] = "global",
|
|
expected_zmq_port: int = 5557,
|
|
metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
|
|
batch_size: int = 0,
|
|
**kwargs,
|
|
) -> list[SearchResult]:
|
|
"""
|
|
Search for nearest neighbors with optional metadata filtering.
|
|
|
|
Args:
|
|
query: Text query to search for
|
|
top_k: Number of nearest neighbors to return
|
|
complexity: Search complexity/candidate list size, higher = more accurate but slower
|
|
beam_width: Number of parallel search paths/IO requests per iteration
|
|
prune_ratio: Ratio of neighbors to prune via approximate distance (0.0-1.0)
|
|
recompute_embeddings: Whether to fetch fresh embeddings from server vs use stored codes
|
|
pruning_strategy: Candidate selection strategy - "global" (default), "local", or "proportional"
|
|
expected_zmq_port: ZMQ port for embedding server communication
|
|
metadata_filters: Optional filters to apply to search results based on metadata.
|
|
Format: {"field_name": {"operator": value}}
|
|
Supported operators:
|
|
- Comparison: "==", "!=", "<", "<=", ">", ">="
|
|
- Membership: "in", "not_in"
|
|
- String: "contains", "starts_with", "ends_with"
|
|
Example: {"chapter": {"<=": 5}, "tags": {"in": ["fiction", "drama"]}}
|
|
**kwargs: Backend-specific parameters
|
|
|
|
Returns:
|
|
List of SearchResult objects with text, metadata, and similarity scores
|
|
"""
|
|
logger.info("🔍 LeannSearcher.search() called:")
|
|
logger.info(f" Query: '{query}'")
|
|
logger.info(f" Top_k: {top_k}")
|
|
logger.info(f" Metadata filters: {metadata_filters}")
|
|
logger.info(f" Additional kwargs: {kwargs}")
|
|
|
|
# Smart top_k detection and adjustment
|
|
# Use PassageManager length (sum of shard sizes) to avoid
|
|
# depending on a massive combined map
|
|
total_docs = len(self.passage_manager)
|
|
original_top_k = top_k
|
|
if top_k > total_docs:
|
|
top_k = total_docs
|
|
logger.warning(
|
|
f" ⚠️ Requested top_k ({original_top_k}) exceeds total documents ({total_docs})"
|
|
)
|
|
logger.warning(f" ✅ Auto-adjusted top_k to {top_k} to match available documents")
|
|
|
|
zmq_port = None
|
|
|
|
start_time = time.time()
|
|
if recompute_embeddings:
|
|
zmq_port = self.backend_impl._ensure_server_running(
|
|
self.meta_path_str,
|
|
port=expected_zmq_port,
|
|
**kwargs,
|
|
)
|
|
del expected_zmq_port
|
|
zmq_time = time.time() - start_time
|
|
logger.info(f" Launching server time: {zmq_time} seconds")
|
|
|
|
start_time = time.time()
|
|
|
|
query_embedding = self.backend_impl.compute_query_embedding(
|
|
query,
|
|
use_server_if_available=recompute_embeddings,
|
|
zmq_port=zmq_port,
|
|
)
|
|
logger.info(f" Generated embedding shape: {query_embedding.shape}")
|
|
embedding_time = time.time() - start_time
|
|
logger.info(f" Embedding time: {embedding_time} seconds")
|
|
|
|
start_time = time.time()
|
|
backend_search_kwargs: dict[str, Any] = {
|
|
"complexity": complexity,
|
|
"beam_width": beam_width,
|
|
"prune_ratio": prune_ratio,
|
|
"recompute_embeddings": recompute_embeddings,
|
|
"pruning_strategy": pruning_strategy,
|
|
"zmq_port": zmq_port,
|
|
}
|
|
# Only HNSW supports batching; forward conditionally
|
|
if self.backend_name == "hnsw":
|
|
backend_search_kwargs["batch_size"] = batch_size
|
|
|
|
# Merge any extra kwargs last
|
|
backend_search_kwargs.update(kwargs)
|
|
|
|
results = self.backend_impl.search(
|
|
query_embedding,
|
|
top_k,
|
|
**backend_search_kwargs,
|
|
)
|
|
search_time = time.time() - start_time
|
|
logger.info(f" Search time in search() LEANN searcher: {search_time} seconds")
|
|
logger.info(f" Backend returned: labels={len(results.get('labels', [[]])[0])} results")
|
|
|
|
enriched_results = []
|
|
if "labels" in results and "distances" in results:
|
|
logger.info(f" Processing {len(results['labels'][0])} passage IDs:")
|
|
# Python 3.9 does not support zip(strict=...); lengths are expected to match
|
|
for i, (string_id, dist) in enumerate(
|
|
zip(results["labels"][0], results["distances"][0])
|
|
):
|
|
try:
|
|
passage_data = self.passage_manager.get_passage(string_id)
|
|
enriched_results.append(
|
|
SearchResult(
|
|
id=string_id,
|
|
score=dist,
|
|
text=passage_data["text"],
|
|
metadata=passage_data.get("metadata", {}),
|
|
)
|
|
)
|
|
|
|
# Color codes for better logging
|
|
GREEN = "\033[92m"
|
|
BLUE = "\033[94m"
|
|
YELLOW = "\033[93m"
|
|
RESET = "\033[0m"
|
|
|
|
# Truncate text for display (first 100 chars)
|
|
display_text = passage_data["text"]
|
|
logger.info(
|
|
f" {GREEN}✓{RESET} {BLUE}[{i + 1:2d}]{RESET} {YELLOW}ID:{RESET} '{string_id}' {YELLOW}Score:{RESET} {dist:.4f} {YELLOW}Text:{RESET} {display_text}"
|
|
)
|
|
except KeyError:
|
|
RED = "\033[91m"
|
|
RESET = "\033[0m"
|
|
logger.error(
|
|
f" {RED}✗{RESET} [{i + 1:2d}] ID: '{string_id}' -> {RED}ERROR: Passage not found!{RESET}"
|
|
)
|
|
|
|
# Apply metadata filters if specified
|
|
if metadata_filters:
|
|
logger.info(f" 🔍 Applying metadata filters: {metadata_filters}")
|
|
enriched_results = self.passage_manager.filter_search_results(
|
|
enriched_results, metadata_filters
|
|
)
|
|
|
|
# Define color codes outside the loop for final message
|
|
GREEN = "\033[92m"
|
|
RESET = "\033[0m"
|
|
logger.info(f" {GREEN}✓ Final enriched results: {len(enriched_results)} passages{RESET}")
|
|
return enriched_results
|
|
|
|
def cleanup(self):
|
|
"""Explicitly cleanup embedding server resources.
|
|
|
|
This method should be called after you're done using the searcher,
|
|
especially in test environments or batch processing scenarios.
|
|
"""
|
|
backend = getattr(self.backend_impl, "embedding_server_manager", None)
|
|
if backend is not None:
|
|
backend.stop_server()
|
|
|
|
# Enable automatic cleanup patterns
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc, tb):
|
|
try:
|
|
self.cleanup()
|
|
except Exception:
|
|
pass
|
|
|
|
def __del__(self):
|
|
try:
|
|
self.cleanup()
|
|
except Exception:
|
|
# Avoid noisy errors during interpreter shutdown
|
|
pass
|
|
|
|
|
|
class LeannChat:
|
|
def __init__(
|
|
self,
|
|
index_path: str,
|
|
llm_config: Optional[dict[str, Any]] = None,
|
|
enable_warmup: bool = False,
|
|
searcher: Optional[LeannSearcher] = None,
|
|
**kwargs,
|
|
):
|
|
if searcher is None:
|
|
self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs)
|
|
self._owns_searcher = True
|
|
else:
|
|
self.searcher = searcher
|
|
self._owns_searcher = False
|
|
self.llm = get_llm(llm_config)
|
|
|
|
def ask(
|
|
self,
|
|
question: str,
|
|
top_k: int = 5,
|
|
complexity: int = 64,
|
|
beam_width: int = 1,
|
|
prune_ratio: float = 0.0,
|
|
recompute_embeddings: bool = True,
|
|
pruning_strategy: Literal["global", "local", "proportional"] = "global",
|
|
llm_kwargs: Optional[dict[str, Any]] = None,
|
|
expected_zmq_port: int = 5557,
|
|
metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
|
|
batch_size: int = 0,
|
|
**search_kwargs,
|
|
):
|
|
if llm_kwargs is None:
|
|
llm_kwargs = {}
|
|
search_time = time.time()
|
|
results = self.searcher.search(
|
|
question,
|
|
top_k=top_k,
|
|
complexity=complexity,
|
|
beam_width=beam_width,
|
|
prune_ratio=prune_ratio,
|
|
recompute_embeddings=recompute_embeddings,
|
|
pruning_strategy=pruning_strategy,
|
|
expected_zmq_port=expected_zmq_port,
|
|
metadata_filters=metadata_filters,
|
|
batch_size=batch_size,
|
|
**search_kwargs,
|
|
)
|
|
search_time = time.time() - search_time
|
|
logger.info(f" Search time: {search_time} seconds")
|
|
context = "\n\n".join([r.text for r in results])
|
|
prompt = (
|
|
"Here is some retrieved context that might help answer your question:\n\n"
|
|
f"{context}\n\n"
|
|
f"Question: {question}\n\n"
|
|
"Please provide the best answer you can based on this context and your knowledge."
|
|
)
|
|
|
|
ask_time = time.time()
|
|
ans = self.llm.ask(prompt, **llm_kwargs)
|
|
ask_time = time.time() - ask_time
|
|
logger.info(f" Ask time: {ask_time} seconds")
|
|
return ans
|
|
|
|
def start_interactive(self):
|
|
print("\nLeann Chat started (type 'quit' to exit)")
|
|
while True:
|
|
try:
|
|
user_input = input("You: ").strip()
|
|
if user_input.lower() in ["quit", "exit"]:
|
|
break
|
|
if not user_input:
|
|
continue
|
|
response = self.ask(user_input)
|
|
print(f"Leann: {response}")
|
|
except (KeyboardInterrupt, EOFError):
|
|
print("\nGoodbye!")
|
|
break
|
|
|
|
def cleanup(self):
|
|
"""Explicitly cleanup embedding server resources.
|
|
|
|
This method should be called after you're done using the chat interface,
|
|
especially in test environments or batch processing scenarios.
|
|
"""
|
|
# Only stop the embedding server if this LeannChat instance created the searcher.
|
|
# When a shared searcher is passed in, avoid shutting down the server to enable reuse.
|
|
if getattr(self, "_owns_searcher", False) and hasattr(self.searcher, "cleanup"):
|
|
self.searcher.cleanup()
|
|
|
|
# Enable automatic cleanup patterns
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc, tb):
|
|
try:
|
|
self.cleanup()
|
|
except Exception:
|
|
pass
|
|
|
|
def __del__(self):
|
|
try:
|
|
self.cleanup()
|
|
except Exception:
|
|
pass
|